With standard DTs as weak learners, GBDT [1]} and XGBoost
[2]}, [3]} can deal with a large amount of
data efficiently and achieve the state-of-the-art performance in many
machine learning problems. They take the ensemble of standard DTs with
boosting, i.e. by defining an objective function and optimizing it with
learning a sequence of DTs. By following the gradient boosting process,
SLM Boost was proposed to ensemble a sequence of SLM trees in
[4]}. All the above discussion can be extended to the
design of the subspace learning regressor (SLR).
